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About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career

The role

We're looking for a versatile and self-directed Machine Learning Engineer to join our newly formed Japan-based Autonomy team — a small, high-agency group focused on developing and adapting AV technology for the unique challenges of the Japanese driving environment.

As one of the first hires on this team, you'll have the opportunity to shape the technical foundation of our local autonomy efforts. You'll work on a broad range of ML challenges — from perception to planning, from model development to evaluation — collaborating with robotics and systems engineers to ensure our stack performs safely, efficiently, and intelligently on Japanese roads.

You'll operate with significant independence, often making day-to-day technical decisions locally while staying aligned with central engineering teams in London. This is a rare opportunity to apply your ML skills in a high-impact, real-world domain, and to help bridge global AV development with local on-the-ground insights.

Key responsibilities:

• Design, implement, and evaluate ML models to improve driving performance in the Japanese road environment.
• Collaborate with robotics engineers on the team to integrate ML models into a real-time AV stack and ensure end-to-end system performance.
• Contribute to data selection, labeling strategy, and targeted dataset improvements that accelerate learning for Japan-specific use cases.
• Work with central ML and Autonomy teams to align model architectures, training pipelines, and evaluation metrics.
• Collaborate with product and deployment stakeholders, helping to translate Japanese road insights into actionable ML development priorities.

About you

You're a pragmatic and hands-on machine learning engineer who thrives in fast-moving, ambiguous environments. You have broad experience developing ML models in complex real-world domains — ideally ones involving robotics, autonomy, or interactive systems.

You're excited by the challenge of making intelligent systems work in the messiness of the real world, and you want your work to contribute to safe, deployable AV technology.

Essential

• Strong experience applying machine learning to real-world problems, including model design, implementation, and evaluation.
• Comfortable working across the ML pipeline — from data to training, testing, debugging, and deployment.
• Excellent software engineering skills, including Python and ML frameworks such as PyTorch or TensorFlow.
• A generalist mindset — you're excited to learn and contribute across different parts of the autonomy stack as needed.
• Comfortable working in a geographically distributed team, with strong communication skills and self-direction.

Desirable

• Experience with autonomous vehicles, robotics, or other ML systems that operate in real-time or physical environments.
• Familiarity with AV-specific ML domains such as vision-based perception, prediction, or imitation learning.
• Understanding of data-centric ML practices, dataset design, and failure analysis in long-tail scenarios.
• Japanese language skills (not required, but useful for engaging with local data, infrastructure, and partners).